Sensitivity-Based Economic NMPC with a Path-Following Approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00308318" target="_blank" >RIV/68407700:21230/17:00308318 - isvavai.cz</a>

Result on the web

<a href="http://www.mdpi.com/2227-9717/5/1/8/htm" target="_blank" >http://www.mdpi.com/2227-9717/5/1/8/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/pr5010008" target="_blank" >10.3390/pr5010008</a>

Result language

angličtina

Original language name

Sensitivity-Based Economic NMPC with a Path-Following Approach

Original language description

We present a sensitivity-based predictor-corrector path-following algorithm for fast nonlinear model predictive control (NMPC) and demonstrate it on a large case study with an economic cost function. The path-following method is applied within the advanced-step NMPC framework to obtain fast and accurate approximate solutions of the NMPC problem. In our approach, we solve a sequence of quadratic programs to trace the optimal NMPC solution along a parameter change. A distinguishing feature of the path-following algorithm in this paper is that the strongly-active inequality constraints are included as equality constraints in the quadratic programs, while the weakly-active constraints are left as inequalities. This leads to close tracking of the optimal solution. The approach is applied to an economic NMPC case study consisting of a process with a reactor, a distillation column and a recycler. We compare the path-following NMPC solution with an ideal NMPC solution, which is obtained by solving the full nonlinear programming problem. Our simulations show that the proposed algorithm effectively traces the exact solution.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA17-26999S" target="_blank" >GA17-26999S: Deep relational learning</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Processes

ISSN

2227-9717

e-ISSN

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Volume of the periodical

5

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

18

Pages from-to

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UT code for WoS article

000398677300007

EID of the result in the Scopus database

2-s2.0-85034249798